Runtime-Incremental Transformer for Reinforcement-Learning-Based Adaptive Control

Abstract:Learning-based adaptive control of robotic manipulators with non-observable friction memory has been addressed by attention- based meta-controllers whose number of attention heads is fixed before training and is tuned by costly offline search. At long memory horizons, such fixed-capacity controllers are prone to catastrophic failures on a sizeable fraction of training seeds. The present paper introduces a runtime mechanism that grows and prunes the heads of the attention block during reinforcement learning, governed by two signals: the effective rank of the on-policy context distribution, which triggers growth when representational capacity becomes insufficient, and the per-head output magnitude, which flags redundant heads for removal. Policy continuity at growth events and a quantitative bound at prune events are established analytically. On a two- link manipulator with Stribeck friction, the proposed mechanism attains full success across all memory regimes, eliminating the long-horizon failure mode and removing the need for offline tuning of the head count.

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2609.13545 [cs.RO]

(or arXiv:2609.13545v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.13545 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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